To understand the behaviour of natural autonomous
systems, research is carried out on artificial autonomous
agents. This paper focuses on how simple behaviours
can be learnt autonomously using a bootstrapping method.
Firstly, a two dimensional Self-Organising Map is realised
which provides the agent's sense of orientation. Once this
relative positioning system has been established, the agent
learns to navigate towards a target using the reinforcement
learning technique of Q-Learning. Since only neural network
processing is used, this technique emulates the distributed
and adaptive information processing found in natural
autonomous systems. Furthermore, due to its generality,
the neural implementation developed is transferable
to other artificial autonomous agents with different sensors
and effector suites.